arXiv subjects

Pengcheng Zhao

Publications and source records attributed to Pengcheng Zhao.

At least 19 recordsLinked to original sources

arXiv2026

Sleep-related forcing of cerebrospinal fluid streaming transport through the third ventricle

Quasi-periodic pressure pulses associated with cardiac and respiratory activity generate reciprocating cerebrospinal fluid (CSF) motion in the human ventricles. While weak and more regular during wakefulness, oscillatory fluid motions have recently been found to be much more exaggerated during NREM sleep, appearing as large flushes in successive pulse trains. The present study investigates whether this forcing can induce greater residual streaming through the human ventricles. Transient CFD simulations were performed in the third ventricle under awake and sleep-related flow pulses. Sleep-related forcing produced clear caudal and rostral preferential pathways and less overall recirculation in the cavity. The streaming flux relative to the stroke volume increased from a low value of about \(0.540\%\) to \(28.4\%\) during sleep. Thus, sleep-related forcing changes not only the magnitude of reciprocating fluid displacement but also the relative contribution of residual streaming. These findings demonstrate differences in hydrodynamic organisation in the third ventricle and highlight the importance of this organisation for intraventricular fluid exchange.

physics.med-ph↗

arXiv2026

Skillful high-resolution weather forecasting independent of physical models

Accurate and timely weather forecasts are critical for high-impact decisions in modern society. Machine-learning-based weather prediction is emerging as an alternative for producing initial conditions, forecasts, and even both in end-to-end systems. These methods deliver predictions faster and often with higher skill than traditional numerical weather prediction (NWP). However, even end-to-end models typically rely on NWP-generated reanalyses for supervision, thereby inheriting the biases and resolution limitations of those NWPs, and limiting adaptation to settings where suitable reanalysis products are unavailable, infrequently updated, or expensive to produce. Here we introduce ObsCast, a regional system that generates both analysis and predictions, without using any NWP-derived data in either training or inference, while still achieving state-of-the-art performance in short-term high-resolution regional modeling. Over the contiguous United States and Europe, ObsCast outperforms operational NWP for near-surface variables through 18 h and produces skillful precipitation forecasts. It provides a simpler and more adaptable route to build and refine regional forecasting services directly from local observations, without the need to develop complex and costly traditional forecasting pipelines.

physics.ao-ph↗

arXiv2026

Decoupling Semantics and Fingerprints: A Universal Representation for AI-Generated Image Detection

Detecting AI-generated images across unseen architectures remains challenging, as existing models often overfit to generator-specific fingerprints and semantic content rather than learning universal forgery traces. We attribute this failure to feature entanglement: detectors learn these factors as a single entangled representation, where universal forgery traces are inextricably confounded with both generator-specific fingerprints and semantic content. Crucially, our spectral analysis reveals that this entanglement is avoidable: distinct generator-specific fingerprints (e.g., GAN stripes vs. Diffusion Model spots) occupy disjoint frequency subspaces and coexist as independent superpositions. Leveraging this physical orthogonality, we propose the Orthogonal Decomposition and Purification Network (ODP-Net) to structurally disentangle these factors. Specifically, ODP-Net employs (1) Instance-aware Orthogonal Decomposition to project features into mutually exclusive subspaces: universal forgery traces, generator-specific fingerprints, and semantic content; (2) Perturbation-based Purification to enforce semantic invariance via cross-sample feature injection; and (3) Manifold Alignment to bridge domain gaps. By explicitly decoupling universal forgery traces from generator-specific fingerprints and semantic content, ODP-Net achieves state-of-the-art performance on unseen architectures (e.g., Stable Diffusion 3), validating that structural disentanglement is key to generalization.

cs.CV↗

arXiv2026

GroundingAnomaly: Spatially-Grounded Diffusion for Few-Shot Anomaly Synthesis

The performance of visual anomaly inspection in industrial quality control is often constrained by the scarcity of real anomalous samples. Consequently, anomaly synthesis techniques have been developed to enlarge training sets and enhance downstream inspection. However, existing methods either suffer from poor integration caused by inpainting or fail to provide accurate masks. To address these limitations, we propose GroundingAnomaly, a novel few-shot anomaly image generation framework. Our framework introduces a Spatial Conditioning Module that leverages per-pixel semantic maps to enable precise spatial control over the synthesized anomalies. Furthermore, a Gated Self-Attention Module is designed to inject conditioning tokens into a frozen U-Net via gated attention layers. This carefully preserves pretrained priors while ensuring stable few-shot adaptation. Extensive evaluations on the MVTec AD and VisA datasets demonstrate that GroundingAnomaly generates high-quality anomalies and achieves state-of-the-art performance across multiple downstream tasks, including anomaly detection, segmentation, and instance-level detection.

cs.CV↗

arXiv2026

Whisper-MLA: Reducing GPU Memory Consumption of ASR Models based on MHA2MLA Conversion

The Transformer-based Whisper model has achieved state-of-the-art performance in Automatic Speech Recognition (ASR). However, its Multi-Head Attention (MHA) mechanism results in significant GPU memory consumption due to the linearly growing Key-Value (KV) cache usage, which is problematic for many applications especially with long-form audio. To address this, we introduce Whisper-MLA, a novel architecture that incorporates Multi-Head Latent Attention (MLA) into the Whisper model. Specifically, we adapt MLA for Whisper's absolute positional embeddings and systematically investigate its application across encoder self-attention, decoder self-attention, and cross-attention modules. Empirical results indicate that applying MLA exclusively to decoder self-attention yields the desired balance between performance and memory efficiency. Our proposed approach allows conversion of a pretrained Whisper model to Whisper-MLA with minimal fine-tuning. Extensive experiments on the LibriSpeech benchmark validate the effectiveness of this conversion, demonstrating that Whisper-MLA reduces the KV cache size by up to 87.5% while maintaining competitive accuracy.

cs.SD↗

arXiv2025

Experimental Demonstration and Transformation Mechanism of Quenchable Two-dimensional Diamond

Two-dimensional (2D) diamond has aroused tremendous interest in nanoelectronics and optoelectronics, owing to its superior properties and flexible characteristics compared to bulk diamond. Despite significant efforts, great challenges lie in the experimental synthesis and transformation conditions of 2D diamond. Herein, we have demonstrated the experimental preparation of high quality 2D diamond with controlled thickness and distinguished properties, realized by laser-heating few-layer graphene in diamond anvil cell. The quenched 2D diamond exhibited narrow T2g Raman peak (linewidth ~3.6 cm-1) and intense photoluminescence of SiV- (linewidth ~6.1 nm) and NV0 centers. In terms of transformation mechanism, atomic structures of hybrid phase interfaces suggested that the intermediate rhombohedral phase subtly mediate hexagonal graphite to cubic diamond transition. Furthermore, the tunable optical bandgap and thermal stability of 2D diamond sensitively depend on its sp3 concentration. We believe our results can shed light on the structural design and preparation of many carbon allotropes and further uncover the underlying transition mechanism.

cond-mat.mtrl-sci↗

arXiv2025

Suspended waveguide-enhanced near-infrared photothermal spectroscopy for ppb-level molecular gas sensing on a chalcogenide chip

On-chip waveguide sensors have attracted significant attention recently due to their potential for high level integration. However, so far on-chip gas sensing based on traditional laser absorption spectroscopy has demonstrated low detection sensitivity, due to weak light-gas interaction over a limited interaction distance. On-chip photothermal spectroscopy (PTS) appears to be a powerful technique to achieve higher sensitivity, its performance is yet constrained to parts-per-million (ppm)-level due to small fraction of evanescent field in the light-gas interaction zone and fast thermal dissipation through the solid substrate. Herein, we demonstrated suspended chalcogenide glass waveguide (ChGW)-enhanced PTS that overcomes these limitations, enabling highly sensitive parts-per-billion (ppb)-level molecular gas sensing. We fabricated a nanoscale suspended ChGW with low loss of 2.6 dB/cm using CMOS-compatible two-step patterning process. By establishing an equivalent PTS model to guide the optimization of the ChGW geometry, we achieved a 4-fold increase in the absorption-induced heat source power and a 10.6-fold decrease in the equivalent heat conductivity, resulting in a 45-fold enhancement in photothermal phase modulation efficiency over the non-suspended waveguides. Combining with a high-contrast waveguide facet-formed Fabry-Perot interferometer, we achieved an unprecedented acetylene detection limit of 330 ppb, a large dynamic range close to 6 orders of magnitude, and a fast response of less than 1 s. The overall system exhibits a noise-equivalent absorption coefficient of 3.8x10-7 cm-1, setting a new benchmark for photonic waveguide gas sensors to the best of our knowledge. This work provides a key advancement towards prototyping an integrated sensor-on-a-chip for highly sensitive and background-free photonic sensing applications.

physics.optics↗

arXiv2025

Studying magnetic circular vortex dichroism effect for photoionization of Rydberg atoms with vortex photons

Rydberg atoms, renowned for their exceptional quantum properties, hold significant importance in quantum physics. The photoionization of Rydberg atoms serves as a critical tool for probing their unique characteristics. In this work, we investigate the photoionization dynamics of hydrogen-like Rydberg alkali atoms interacting with vortex photons-a class of structured light carrying intrinsic orbital angular momentum. This process gives rise to novel quantum phenomena distinct from conventional photoionization processes. Our results reveal that vortex photons exhibit exceptional sensitivity to the magnetic moments of Rydberg atoms, positioning them as a powerful spectroscopic tool for investigating Rydberg magnetism. It is also demonstrated that the initial photon energy must be carefully selected to observe significant experimental results. Furthermore, the photoionization process displays strong angular momentum selectivity, preferentially favoring configurations where the photon total angular momentum and atomic magnetic moment are aligned. This pronounced asymmetry directly manifests the chiral nature of the vortex photon-Rydberg atom collisions.

physics.atom-ph↗

arXiv2025

Three-dimensional trapping of circular Rydberg atoms by a superimposed vortex light beam

We propose to trap circular Rydberg atoms (CRAs) by a ponderomotive potential well formed with a superimposed vortex light beam. We calculate analytically the ponderomotive potential energy for a Bessel vortex light beam. We work out a corrected version of the classical circular orbit approximation for a CRA which fits the exact result much better than the usual approximation. We reveal the three-dimensional characteristics of the potential well for some benchmark values of the CRA principal quantum number and beam parameters such as the frequency, the opening angle and topological charge of the vortex. We investigate how we can achieve similar trapping effects for different principal quantum numbers by varying beam parameters. The potential provides a lattice structure in the beam axis where one CRA could be trapped at each lattice site.

physics.atom-ph↗

arXiv2025

SolarSeer: Ultrafast and accurate 24-hour solar irradiance forecasts outperforming numerical weather prediction across the USA

Accurate 24-hour solar irradiance forecasting is essential for the safe and economic operation of solar photovoltaic systems. Traditional numerical weather prediction (NWP) models represent the state-of-the-art in forecasting performance but rely on computationally costly data assimilation and solving complicated partial differential equations (PDEs) that simulate atmospheric physics. Here, we introduce SolarSeer, an end-to-end large artificial intelligence (AI) model for solar irradiance forecasting across the Contiguous United States (CONUS). SolarSeer is designed to directly map the historical satellite observations to future forecasts, eliminating the computational overhead of data assimilation and PDEs solving. This efficiency allows SolarSeer to operate over 1,500 times faster than traditional NWP, generating 24-hour cloud cover and solar irradiance forecasts for the CONUS at 5-kilometer resolution in under 3 seconds. Compared with the state-of-the-art NWP in the CONUS, i.e., High-Resolution Rapid Refresh (HRRR), SolarSeer significantly reduces the root mean squared error of solar irradiance forecasting by 27.28% in reanalysis data and 15.35% across 1,800 stations. SolarSeer also effectively captures solar irradiance fluctuations and significantly enhances the first-order irradiance difference forecasting accuracy. SolarSeer's ultrafast, accurate 24-hour solar irradiance forecasts provide strong support for the transition to sustainable, net-zero energy systems.

cs.LG↗

arXiv2025

LD-DETR: Loop Decoder DEtection TRansformer for Video Moment Retrieval and Highlight Detection

Video Moment Retrieval and Highlight Detection aim to find corresponding content in the video based on a text query. Existing models usually first use contrastive learning methods to align video and text features, then fuse and extract multimodal information, and finally use a Transformer Decoder to decode multimodal information. However, existing methods face several issues: (1) Overlapping semantic information between different samples in the dataset hinders the model's multimodal aligning performance; (2) Existing models are not able to efficiently extract local features of the video; (3) The Transformer Decoder used by the existing model cannot adequately decode multimodal features. To address the above issues, we proposed the LD-DETR model for Video Moment Retrieval and Highlight Detection tasks. Specifically, we first distilled the similarity matrix into the identity matrix to mitigate the impact of overlapping semantic information. Then, we designed a method that enables convolutional layers to extract multimodal local features more efficiently. Finally, we fed the output of the Transformer Decoder back into itself to adequately decode multimodal information. We evaluated LD-DETR on four public benchmarks and conducted extensive experiments to demonstrate the superiority and effectiveness of our approach. Our model outperforms the State-Of-The-Art models on QVHighlight, Charades-STA and TACoS datasets. Our code is available at https://github.com/qingchen239/ld-detr.

cs.CV↗

arXiv2024

OMG-HD: A High-Resolution AI Weather Model for End-to-End Forecasts from Observations

In recent years, Artificial Intelligence Weather Prediction (AIWP) models have achieved performance comparable to, or even surpassing, traditional Numerical Weather Prediction (NWP) models by leveraging reanalysis data. However, a less-explored approach involves training AIWP models directly on observational data, enhancing computational efficiency and improving forecast accuracy by reducing the uncertainties introduced through data assimilation processes. In this study, we propose OMG-HD, a novel AI-based regional high-resolution weather forecasting model designed to make predictions directly from observational data sources, including surface stations, radar, and satellite, thereby removing the need for operational data assimilation. Our evaluation shows that OMG-HD outperforms both the European Centre for Medium-Range Weather Forecasts (ECMWF)'s high-resolution operational forecasting system, IFS-HRES, and the High-Resolution Rapid Refresh (HRRR) model at lead times of up to 12 hours across the contiguous United States (CONUS) region. We achieve up to a 13% improvement on RMSE for 2-meter temperature, 17% on 10-meter wind speed, 48% on 2-meter specific humidity, and 32% on surface pressure compared to HRRR. Our method shows that it is possible to use AI-driven approaches for rapid weather predictions without relying on NWP-derived weather fields as model input. This is a promising step towards using observational data directly to make operational forecasts with AIWP models.

physics.ao-ph↗

arXiv2024

Multimodal Class-aware Semantic Enhancement Network for Audio-Visual Video Parsing

The Audio-Visual Video Parsing task aims to recognize and temporally localize all events occurring in either the audio or visual stream, or both. Capturing accurate event semantics for each audio/visual segment is vital. Prior works directly utilize the extracted holistic audio and visual features for intra- and cross-modal temporal interactions. However, each segment may contain multiple events, resulting in semantically mixed holistic features that can lead to semantic interference during intra- or cross-modal interactions: the event semantics of one segment may incorporate semantics of unrelated events from other segments. To address this issue, our method begins with a Class-Aware Feature Decoupling (CAFD) module, which explicitly decouples the semantically mixed features into distinct class-wise features, including multiple event-specific features and a dedicated background feature. The decoupled class-wise features enable our model to selectively aggregate useful semantics for each segment from clearly matched classes contained in other segments, preventing semantic interference from irrelevant classes. Specifically, we further design a Fine-Grained Semantic Enhancement module for encoding intra- and cross-modal relations. It comprises a Segment-wise Event Co-occurrence Modeling (SECM) block and a Local-Global Semantic Fusion (LGSF) block. The SECM exploits inter-class dependencies of concurrent events within the same timestamp with the aid of a new event co-occurrence loss. The LGSF further enhances the event semantics of each segment by incorporating relevant semantics from more informative global video features. Extensive experiments validate the effectiveness of the proposed modules and loss functions, resulting in a new state-of-the-art parsing performance.

cs.CV↗

arXiv2024

ADAF: An Artificial Intelligence Data Assimilation Framework for Weather Forecasting

The forecasting skill of numerical weather prediction (NWP) models critically depends on the accurate initial conditions, also known as analysis, provided by data assimilation (DA). Traditional DA methods often face a trade-off between computational cost and accuracy due to complex linear algebra computations and the high dimensionality of the model, especially in nonlinear systems. Moreover, processing massive data in real-time requires substantial computational resources. To address this, we introduce an artificial intelligence-based data assimilation framework (ADAF) to generate high-quality kilometer-scale analysis. This study is the pioneering work using real-world observations from varied locations and multiple sources to verify the AI method's efficacy in DA, including sparse surface weather observations and satellite imagery. We implemented ADAF for four near-surface variables in the Contiguous United States (CONUS). The results indicate that ADAF surpasses the High Resolution Rapid Refresh Data Assimilation System (HRRRDAS) in accuracy by 16% to 33% for near-surface atmospheric conditions, aligning more closely with actual observations, and can effectively reconstruct extreme events, such as tropical cyclone wind fields. Sensitivity experiments reveal that ADAF can generate high-quality analysis even with low-accuracy backgrounds and extremely sparse surface observations. ADAF can assimilate massive observations within a three-hour window at low computational cost, taking about two seconds on an AMD MI200 graphics processing unit (GPU). ADAF has been shown to be efficient and effective in real-world DA, underscoring its potential role in operational weather forecasting.

physics.ao-ph↗

arXiv2024

QTG-VQA: Question-Type-Guided Architectural for VideoQA Systems

In the domain of video question answering (VideoQA), the impact of question types on VQA systems, despite its critical importance, has been relatively under-explored to date. However, the richness of question types directly determines the range of concepts a model needs to learn, thereby affecting the upper limit of its learning capability. This paper focuses on exploring the significance of different question types for VQA systems and their impact on performance, revealing a series of issues such as insufficient learning and model degradation due to uneven distribution of question types. Particularly, considering the significant variation in dependency on temporal information across different question types, and given that the representation of such information coincidentally represents a principal challenge and difficulty for VideoQA as opposed to ImageQA. To address these challenges, we propose QTG-VQA, a novel architecture that incorporates question-type-guided attention and adaptive learning mechanism. Specifically, as to temporal-type questions, we design Masking Frame Modeling technique to enhance temporal modeling, aimed at encouraging the model to grasp richer visual-language relationships and manage more intricate temporal dependencies. Furthermore, a novel evaluation metric tailored to question types is introduced. Experimental results confirm the effectiveness of our approach.

cs.CV↗

arXiv2024

WeatherReal: A Benchmark Based on In-Situ Observations for Evaluating Weather Models

In recent years, AI-based weather forecasting models have matched or even outperformed numerical weather prediction systems. However, most of these models have been trained and evaluated on reanalysis datasets like ERA5. These datasets, being products of numerical models, often diverge substantially from actual observations in some crucial variables like near-surface temperature, wind, precipitation and clouds - parameters that hold significant public interest. To address this divergence, we introduce WeatherReal, a novel benchmark dataset for weather forecasting, derived from global near-surface in-situ observations. WeatherReal also features a publicly accessible quality control and evaluation framework. This paper details the sources and processing methodologies underlying the dataset, and further illustrates the advantage of in-situ observations in capturing hyper-local and extreme weather through comparative analyses and case studies. Using WeatherReal, we evaluated several data-driven models and compared them with leading numerical models. Our work aims to advance the AI-based weather forecasting research towards a more application-focused and operation-ready approach.

physics.ao-ph↗

arXiv2024

Anomalous Raman Response in 2D Magnetic FeTe under Uniaxial Strain: Tetragonal and Hexagonal Polymorphs

Two-dimensional (2D) Fe-chalcogenides have emerged with rich structures, magnetisms and superconductivities, which sparked the growing research interests in the torturous transition mechanism and tunable properties for their potential applications in nanoelectronics. Uniaxial strain can produce a lattice distortion to study symmetry breaking induced exotic properties in 2D magnets. Herein, the anomalous Raman spectrum of 2D tetragonal (t-) and hexagonal (h-) FeTe were systematically investigated via uniaxial strain engineering strategy. We found that both t- and h-FeTe keep the structural stability under different uniaxial tensile or compressive strain up to +/- 0.4%. Intriguingly, the lattice vibrations along both in-plane and out-of-plane directions exceptionally hardened (softened) under tensile (compressive) strain, distinguished from the behaviors of many conventional 2D systems. Furthermore, the difference in thickness-dependent strain effect can be well explained by their structural discrepancy between two polymorphs of FeTe. Our results could provide a unique platform to elaborate the vibrational properties of many novel 2D materials.

cond-mat.mtrl-sci↗

arXiv2024

Audio-Infused Automatic Image Colorization by Exploiting Audio Scene Semantics

Automatic image colorization is inherently an ill-posed problem with uncertainty, which requires an accurate semantic understanding of scenes to estimate reasonable colors for grayscale images. Although recent interaction-based methods have achieved impressive performance, it is still a very difficult task to infer realistic and accurate colors for automatic colorization. To reduce the difficulty of semantic understanding of grayscale scenes, this paper tries to utilize corresponding audio, which naturally contains extra semantic information about the same scene. Specifically, a novel and pluggable audio-infused automatic image colorization (AIAIC) method is proposed, which consists of three stages. First, we take color image semantics as a bridge and pretrain a colorization network guided by color image semantics. Second, the natural co-occurrence of audio and video is utilized to learn the color semantic correlations between audio and visual scenes. Third, the implicit audio semantic representation is fed into the pretrained network to finally realize the audio-guided colorization. The whole process is trained in a self-supervised manner without human annotation. Experiments demonstrate that audio guidance can effectively improve the performance of automatic colorization, especially for some scenes that are difficult to understand only from visual modality.

cs.CV↗